mlsanityData to 7 Oct 2026

Large Language Models for the Automated Analysis of Optimization Algorithms

arXiv 2402.08472 ↗First posted 13 Feb 2024cs.AIcited by 5

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Abstract (arXiv)

The ability of Large Language Models (LLMs) to generate high-quality text and code has fuelled their rise in popularity. In this paper, we aim to demonstrate the potential of LLMs within the realm of optimization algorithms by integrating them into STNWeb. This is a web-based tool for the generation of Search Trajectory Networks (STNs), which are visualizations of optimization algorithm behavior. Although visualizations produced by STNWeb can be very informative for algorithm designers, they often require a certain level of prior knowledge to be interpreted. In an attempt to bridge this knowledge gap, we have incorporated LLMs, specifically GPT-4, into STNWeb to produce extensive written reports, complemented by automatically generated plots, thereby enhancing the user experience and reducing the barriers to the adoption of this tool by the research community. Moreover, our approach can be expanded to other tools from the optimization community, showcasing the versatility and potential of LLMs in this field.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 36 of its 48 references that are in the map. First posted on arXiv 13 Feb 2024.

Builds on

36 of 48 references.

References · 36

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24 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 1.

5 papers.

  1. 2024A Systematic Survey on Large Language Models for Algorithm Designcited by 65
  2. 2025A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solvingcited by 9
  3. 2025Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithmscited by 5
  4. 2025Behavior and Representation in Open-Weight Large Language Models for Combinatorial Optimization: From Feature Extraction to Algorithm Selectioncited by 0
  5. 2026Constraint-aware Path Planning from Natural Language Instructions Using Large Language Modelscited by 0